SensoMineR

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SensoMineR is an R package for statistical analysis of sensory data, offering methods for product...
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### Install RExcel via R GUI

Source: http://sensominer.free.fr/Excel.html

Use these commands in the R GUI to install the RExcel installer and statconnDCOM if R 2.11.1 is already present.

```R
install.packages("RExcelInstaller")
library(RExcelInstaller)
installRExcel()
library(rcom)
installstatconnDCOM()
```

--------------------------------

### Install SensoMineR

Source: http://sensominer.free.fr/GUI.html

Run this command in R to install the package. Requires an active internet connection.

```R
source("http://sensominer.free.fr/install-senso.r")
```

--------------------------------

### MAGICSORT: Example 1 - Data Description

Source: http://sensominer.free.fr/magicsortex1.htm

This example demonstrates how to use the decat and coltable functions from SensoMineR to describe product data. It involves data preparation and visualization of descriptive statistics.

```R
data(chocolates)
resdecat<-decat(chocolates, formul = "~Product", firstvar = 5, graph = FALSE)
coltable(magicsort(resdecat$tabT) , level.lower = -1.96, level.upper = 1.96, main.title = "Products' description")
```

--------------------------------

### MAGICSORT: Example 2 - P-value table

Source: http://sensominer.free.fr/magicsortex2.htm

This example demonstrates how to use the magicsort function to sort P-values and then display them in a table using coltable. It requires the 'chocolates' dataset and the paneliperf function.

```R
data(chocolates)
resperf<-paneliperf(chocolates, formul ="~Product+Panelist+Product:Panelist", formul.j = "~Product", col.j = 1, firstvar = 5, lastvar = 12, synthesis = FALSE, graph = FALSE)
res.sort = magicsort(resperf$prob.ind, method = "median")
coltable(res.sort, main.title = "P-values of the F-test by panelist")
```

--------------------------------

### Load CHOCOLATES Data

Source: http://sensominer.free.fr/chocolates.htm

Loads the CHOCOLATES dataset, which includes sensochoc, sensopanels, and hedochoc data frames. Ensure the 'SensoMineR' package is installed and loaded.

```r
data(chocolates)
```

--------------------------------

### GET /data/chocolates

Source: http://sensominer.free.fr/chocolates.htm

Retrieves the chocolates dataset containing sensory and hedonic evaluations.

```APIDOC
## GET /data/chocolates

### Description
Retrieves the chocolates dataset, which is divided into three subsets: sensochoc, sensopanels, and hedochoc.

### Method
GET

### Endpoint
/data/chocolates

### Response
#### Success Response (200)
- **sensochoc** (data frame) - 348 rows (29 panelists, 6 products, 2 sessions) and 19 columns (5 qualitative, 14 sensory descriptors).
- **sensopanels** (data frame) - 6 rows (6 products) and 98 columns (7 panels and 14 descriptors).
- **hedochoc** (data frame) - 6 rows (6 products) and 222 columns (one per consumer).
```

--------------------------------

### Launch SensoMineR Interface

Source: http://sensominer.free.fr/GUI.html

Load the Rcmdr environment to access the SensoMineR menu.

```R
library(Rcmdr)
```

--------------------------------

### Access Function Help

Source: http://sensominer.free.fr/GUI.html

Use this command in the R console to view documentation for the decat function.

```R
?decat
```

--------------------------------

### Prepare and Perform Preference Mapping (Prefmap)

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/prefmap_napping.txt

Reads preference mapping data and performs cartography. 'regmod' specifies the regression model, 'resolution' sets the output resolution, and 'level' controls contour lines.

```R
pref_nappe <- read.table("prefmap_conso.csv", header=TRUE, sep=";", na.strings="", dec=",",row.names=1)
prefnapping=carto(pref_nappe[,1:2],pref_nappe[,-c(1:2)], regmod=1,coord = c(1,2), resolution =200,level =0,nb.clusters=0,label.j=FALSE)
```

--------------------------------

### Bootstrap Analysis for Napping Data

Source: http://sensominer.free.fr/desc-napping.html

Perform a bootstrap analysis on sensory data using the 'boot' function with the 'napping' method. This helps in generating confidence ellipses for product positions on factor maps. Key parameters include the dataset, number of dimensions, number of simulations, and confidence level.

```r
res.boot<-boot(biscuits[,c(1:36)], method = "napping", axes = 1:2, ncp=3,  
            scale = FALSE, group = c(rep(2,18)),nbsim = 200,level.conf = 0.95)  
            #biscuits: the data set used  
            #ncp: number of dimensions kept for the bootstrap  
            #nbsim number of simulations 
```

--------------------------------

### Perform MFA and Visualization in R

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/napping.txt

Imports data, executes Multiple Factor Analysis, and generates various plots for groups, axes, variables, and individuals.

```R
library(SensoMineR)
#importation
cock_nappe <- read.table("AFM_nappes.csv", header=TRUE, sep=";", na.strings="", dec=",",row.names=1)

#FMA
cock_nappe.MFA<-cock_nappe[, c("X1", "Y1", "X2", "Y2", "X3", "Y3", "X4", "Y4", "X5", "Y5", "X6", "Y6", "X7", "Y7", "X8", "Y8", "X9", "Y9", "X11", "Y11", "X12", "Y12")]
res<-MFA(cock_nappe.MFA, group=c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), type=c("c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c"), ncp=5, 
name.group=c("Gc1","Gc2", "Gc3", "Gc4", "Gc5", "Gc6", "Gc7", "Gc8", "Gc9", "Gc10", "Gc11"), graph=FALSE)

plot.MFA(res, axes=c(1, 2), choix="group", lab.grpe=TRUE, title="")
plot.MFA(res, axes=c(1, 2), choix="axes", habillage="group", title="")
plot.MFA(res, axes=c(1, 2), choix="var", lab.var=TRUE, habillage="group", lim.cos2.var=0)
plot.MFA(res, axes=c(1, 2), choix="ind", lab.ind.moy=TRUE, lab.par=TRUE, habillage="ind", title="")
```

--------------------------------

### Import Tab-Separated Data

Source: http://sensominer.free.fr/tutorialq5.htm

Use this code to import tab-separated text files into SensoMineR. Ensure your data is saved as a .txt file with tab separators, descriptors on the first row, and '.' as the decimal point.

```R
mydata <-read.table("C:/Mon_R/myfile.txt", header=T, sep="\t", quote="", dec=".")
```

--------------------------------

### Import and Prepare Data for MFA

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/prefmap_napping.txt

Reads CSV data and selects specific columns for MFA analysis. Ensure the CSV uses ';' as a separator, ',' as a decimal, and has row names.

```R
cock_nappe <- read.table("AFM_nappes.csv", header=TRUE, sep=";", na.strings="", dec=",",row.names=1)
cock_nappe.MFA<-cock_nappe[, c("X1", "Y1", "X2", "Y2", "X3", "Y3", "X4", "Y4", "X5", "Y5", "X6", "Y6", "X7", "Y7", "X8", "Y8", "X9", "Y9", "X11", "Y11", "X12", "Y12")]
```

--------------------------------

### Load Cocktail Dataset

Source: http://sensominer.free.fr/cocktail.htm

Load the cocktail dataset into the R environment.

```R
data(cocktail)
```

--------------------------------

### Visualize Panelist Performance with coltable

Source: http://sensominer.free.fr/coltable_example2.htm

Uses paneliperf to calculate performance metrics and coltable to display p-values from the F-test.

```R
data(chocolates)  
resperf<-paneliperf(chocolates, formul = "~Product+Panelist+Product:Panelist", formul.j = "~Product", col.j = 1, firstvar = 5, lastvar = 12, synthesis = FALSE, graph = FALSE)  
resperfprob<-magicsort(resperf$prob.ind, method = "median")  
coltable(resperfprob, level.lower = 0.05, level.upper = 1, main.title = "P-value of the F-test (by panelist)")
```

--------------------------------

### Visualize sensory data with coltable

Source: http://sensominer.free.fr/coltable_example1.htm

Uses the decat function to process sensory data and coltable to generate a formatted table with significance levels.

```R
data(chocolates)  
resdecat<-decat(chocolates, formul = "~Product+Panelist", firstvar = 5, graph = FALSE)  
coltable(magicsort(resdecat$tabT), level.lower = -1.96, level.upper = 1.96, main.title = "Products' description")
```

--------------------------------

### FAST: Factorial Approach for Sorting Task data

Source: http://sensominer.free.fr/fast.htm

This section details the Factorial Approach for Sorting Task data (FAST) method, which performs Multiple Factor Analysis combined with Procrustean Analysis.

```APIDOC
## FAST: Factorial Approach for Sorting Task data

### Description
Performs Multiple Factor Analysis combined with Procrustean Analysis.

### Details
Perform Factorial Approach for Sorting Task data (FAST) on a table where the rows (i) are products and the columns (j) are consumers. A cell (i,j) corresponds either to the number of the group to which the product i belongs to for the consumer j, or, in the case of "qualified" categorization, to the sequence of words associted to the group of which the product belongs to (i) for the consumer j.

### Results
Individuals factor map | Variables' factor map | Groups' representation | Nb of groups formed from sorting tasks | Confidence ellipses  
---|---|---|---|---
Nb of products per group |  |  |  |  
---|---|---|---|---

### Outputs
A list containing the following elements: 
- `eig`: a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance  
- `var`: a list of matrices containing all the results for the categories (coordinates, square cosine, contributions, v.test)  
- `ind`: a list of matrices containing all the results for the products (coordinates, square cosine, contributions)  
- `group`: a list of matrices containing all the results for consumers (coordinates, square cosine, contributions)  
- `cooccur`: the reordered co-occurrence matrix among products  
- `reord`: the reordered matrix products*consumers  
- `cramer`: the Cramer's V matrix between all the consumers  
- `call`: a list with some statistics
```

--------------------------------

### Perform Partial Multiple Factor Analysis (PMFA)

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/prefmap_napping.txt

Executes PMFA on selected data columns. 'matrice.illu' can be used for illustration matrices, and 'lim' controls the limits for plotting.

```R
cock_nappe.pmfa = pmfa(
cock_nappe[,c("X1", "Y1", "X2", "Y2", "X3", "Y3", "X4", "Y4", "X5", "Y5", "X6", "Y6", "X7", "Y7", "X8", "Y8", "X9", "Y9", "X11", "Y11", "X12", "Y12")],
matrice.illu = NULL, coord=c(1,2), lim = c(60,40), graph.mfa=1,graph.ind=1,dilat=TRUE)
```

--------------------------------

### Prepare Averaged Data for PCA

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Selects averaged data per product for PCA, excluding initial identifier columns and reassigning row names.

```R
average=moyennes[1:16,]
rownames(average)=average[,2]
```

--------------------------------

### Perform Preference Mapping in R

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/napping.txt

Imports preference data and generates a preference map using the carto function.

```R
#Prefmap
pref_nappe <- read.table("prefmap_conso.csv", header=TRUE, sep=";", na.strings="", dec=",",row.names=1)
prefnapping=carto(pref_nappe[,1:2],pref_nappe[,-c(1:2)], regmod=1,coord = c(1,2), resolution =200,level =0,nb.clusters=0,label.j=FALSE)
```

--------------------------------

### Perform Multiple Factor Analysis (MFA)

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/prefmap_napping.txt

Conducts MFA on prepared data with specified group sizes, types, and number of components. Set graph=TRUE to visualize results.

```R
res<-MFA(cock_nappe.MFA, group=c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), type=c("c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c"), ncp=5, 
name.group=c("Gc1","Gc2", "Gc3", "Gc4", "Gc5", "Gc6", "Gc7", "Gc8", "Gc9", "Gc10", "Gc11"), graph=FALSE)
```

--------------------------------

### Average Data by Panelist and Product

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Averages data by product and panelist, centering and scaling if specified. Use when raw data needs aggregation before analysis.

```R
moyennes=scalebypanelist(classique,firstvar=5,center=FALSE,scale=FALSE,col.p=4,col.j=2)
```

--------------------------------

### Visualize PCA Results - Individuals

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Generates a plot representing products (individuals) on the first two principal components of the PCA.

```R
plot.PCA(res_pcamoy, axes=c(1, 2), choix="ind", habillage="none", col.ind="black", col.ind.sup="blue", col.quali="magenta", 
  label=c("ind", "ind.sup", "quali"), title="")
```

--------------------------------

### Construct INDSCAL Model for Napping Data

Source: http://sensominer.free.fr/indscal.htm

Use this function to construct the Indscal model specifically adapted for Napping data type, where product coordinates are used as input.

```r
indscal(napping.don, napping.words)
```

--------------------------------

### Draw Bar Plots for Each Product

Source: http://sensominer.free.fr/products-characterization.html

Use the _barrow_ function to draw bar plots for each product, providing a graphical visualization of product characteristics.

```r
barrow()
```

--------------------------------

### Draw Boxplots for Each Product

Source: http://sensominer.free.fr/products-characterization.html

Use the _boxprod_ function to draw boxplots for each product and each descriptor, allowing for the visualization of global tendencies and aberrant data.

```r
boxprod()
```

--------------------------------

### Import Data with Row Names

Source: http://sensominer.free.fr/tutorialq5.htm

Import data into SensoMineR and specify the first column as row names, which can be useful for identifying individuals. This uses the row.names option within the read.table function.

```R
mydata <-read.table("C:/Mon_R/myfile.txt", header=T, sep="\t", quote="", dec=".", row.names=1)
```

--------------------------------

### Compute Means per Category with AVERAGETABLE

Source: http://sensominer.free.fr/averagetable.htm

Use this to compute the simple means per category for a specific variable, such as products, in your dataset. The 'method' parameter should be set to 'mean' and the 'formul' can be restricted to the main variable of interest.

```R
averagetable(chocolates, method="mean", formul="~Product", firstvar=5)
```

--------------------------------

### Perform MFA and Draw Confidence Ellipses

Source: http://sensominer.free.fr/products-characterization.html

The _panellipse.session_ function performs Multiple Factor Analysis (MFA) and draws confidence ellipses, useful for checking product discrimination and panel repeatability.

```r
panellipse.session()
```

--------------------------------

### GRAPHINTER: Graphical display of interaction

Source: http://sensominer.free.fr/graphinter.htm

The GRAPHINTER function displays the interaction between two qualitative variables, typically product and session variables. It requires a balanced or near-balanced dataset.

```APIDOC
## GRAPHINTER

### Description
This function is designed to display the interaction between two qualitative variables, in most cases the product and the session variables. The data set must be balanced (or not too much unbalanced).

### Outputs
A list containing the following elements:
- `prod`: a data frame of dimension (p,q), the means over the panelists and the sessions for the p products and the q sensory descriptors
- `seance`: as many matrices of dimension (p,q) as there are sessions, the means over the panelists for the p products, the q sensory descriptors and for each session
```

--------------------------------

### FASNT: Factorial Approach for Sorting Napping Task data

Source: http://sensominer.free.fr/fasnt.htm

The FASNT function analyzes a table where rows represent products and columns represent consumer coordinates for napping and partitioning variables. It groups consumers based on their responses.

```APIDOC
## FASNT /api/fasnt

### Description
Perform Factorial Approach for Sorting Napping Task data (FASNT) on a table where the rows (i) are products and the columns (j) are for each consumer the coordinates of the products on the tablecloth associated with napping on the one hand and the partitionning variable associated with categorization on the other hand. The columns are grouped by consumer. For the partitionning variable, the label associated with a group can be an arbitrary label (for example G1 for group 1, etc.) or the words associated with the group in the case of qualified sorted napping.

### Method
POST

### Endpoint
/api/fasnt

### Parameters
#### Request Body
- **data** (table) - Required - A table where rows are products and columns are consumer coordinates for napping and partitioning variables.
- **napping_variable_label** (string) - Optional - Label for the napping variable.
- **partitioning_variable_label** (string) - Optional - Label for the partitioning variable.

### Request Example
```json
{
  "data": [
    ["ProductA", 1, 2, 3, 4, 5, 6],
    ["ProductB", 2, 3, 4, 5, 6, 7],
    ["ProductC", 3, 4, 5, 6, 7, 8]
  ],
  "napping_variable_label": "Napping",
  "partitioning_variable_label": "Category"
}
```

### Response
#### Success Response (200)
- **eig** (matrix) - A matrix containing eigenvalues, variance percentages, and cumulative variance percentages.
- **ind** (list) - A list of matrices containing results for products (coordinates, square cosine, contributions).
- **quali.var** (list) - A list of matrices containing results for categorization categories (coordinates, square cosine, contributions, v.tests).
- **quanti.var** (list) - A list of matrices containing results for napping (coordinates, square cosine, contributions, v.test).
- **group** (list) - A list of matrices containing results for consumers (coordinates, square cosine, contributions).
- **indicator** (list) - A list of matrices containing different indicators for napping and categorization.
- **textual** (list) - The results of the textual analysis for the products.
- **validity** (list) - Elements of validity calculated for the first eigenvalue and ellipses.
- **call** (list) - A list with some statistics.

#### Response Example
```json
{
  "eig": [[10.5, 50.0, 50.0], [5.0, 25.0, 75.0]],
  "ind": [{"coordinates": [1.2, 0.5], "square_cosine": 0.8, "contributions": 0.3}],
  "quali.var": [{"coordinates": [0.8, -0.2], "square_cosine": 0.7, "contributions": 0.2, "v.tests": 1.5}],
  "quanti.var": [{"coordinates": [-0.5, 1.0], "square_cosine": 0.9, "contributions": 0.4, "v.test": 2.1}],
  "group": [{"coordinates": [0.1, -0.3], "square_cosine": 0.6, "contributions": 0.1}],
  "indicator": [{"indicator_name": "Indicator1", "value": 0.95}],
  "textual": [{"product": "ProductA", "analysis": "Result1"}],
  "validity": [{"eigenvalue": 10.5, "ellipse_value": 0.9}],
  "call": [{"parameter": "value"}]
}
```
```

--------------------------------

### Evaluate Panel Performance

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Calculates and displays panel performance metrics, including p-values for product-panelist interactions and variability.

```R
respanelperf=panelperf(classique[,c("Session", "Panelist", "Rank", "Juice", "color.intensity", "odor.intensity", 
  "strongness", "sweet", "acidity", "bitterness", "odor.orange", "odor.banana", "odor.mango", "odor.lemon", 
  "persistence", "pulp", "thickness","hedonic")],firstvar=5,formul=~"Juice+Panelist+Juice:Panelist",random=1)
respanelperf$p.value
coltable(magicsort(respanelperf$p.value, sort.mat = respanelperf$p.value[,1], bycol = FALSE,method = "median"),
  main.title = "Panel performance (sorted by product P-value)")
respanelperf$complete
respanelperf$variability
respanelperf$res
```

--------------------------------

### NAPPESORTPLOT: Plot consumers' sorted tablecloth

Source: http://sensominer.free.fr/nappesortplot.htm

This endpoint is used to plot the consumers' sorted tablecloth, providing a visual representation of sorted data.

```APIDOC
## NAPPESORTPLOT /plot/consumers/sorted_tablecloth

### Description
Plots consumers' sorted tablecloth.

### Method
GET

### Endpoint
/plot/consumers/sorted_tablecloth

### Parameters
#### Query Parameters
- **panelists_tablecloth** (string) - Required - The data representing the panelists' tablecloth.

### Response
#### Success Response (200)
- **plot_url** (string) - URL to the generated plot.

#### Response Example
{
  "plot_url": "/path/to/your/plot.png"
}
```

--------------------------------

### Triangle Test Functions

Source: http://sensominer.free.fr/triangle.htm

This section details the functions available for performing triangle tests, including design construction and result analysis.

```APIDOC
## triangle.design

### Description
Constructs a design for a given number of products, a given number of panelists and a given number of products per panelist.

### Method
Not specified

### Endpoint
Not specified

## triangle.pair.test

### Description
Returns the p-value associated with a given number of panelists and a given number of good answers for a triangle test with two products.

### Method
Not specified

### Endpoint
Not specified

## triangle.test

### Description
Returns different results (including a matrix with the p-value of the triangle test for each pair of products) associated with a given number of panelists and a given number of good answers for a triangle test.

### Method
Not specified

### Endpoint
Not specified
```

--------------------------------

### MFA with Supplementary Descriptors

Source: http://sensominer.free.fr/desc-napping.html

Use this code to perform MFA with supplementary descriptors. Ensure data is correctly formatted and specify group types and supplementary group numbers.

```R
res.mfa<-MFA (data[,c(2:60)], group=c(rep(2,18),23), type = c(rep("c",18),"s"),  
            ncp = 36, name.group = c(c(1:18),"desc"), num.group.sup = 19, axes = c(1,2))  
            #type : "s" for centered and scaled continuous variables 
```

--------------------------------

### Perform Analysis of Variance for Descriptors

Source: http://sensominer.free.fr/products-characterization.html

The _decat_ function performs analyses of variance for each descriptor to identify which ones are most characteristic for all products and for specific products.

```r
decat()
```

--------------------------------

### COLTABLE Function

Source: http://sensominer.free.fr/coltable.htm

Documentation for the COLTABLE function which colors cells of a data frame based on two threshold levels.

```APIDOC
## COLTABLE

### Description
Returns a colored display of a data frame according to 2 threshold levels. This function is very useful especially when there are a lot of values to check.
```

--------------------------------

### Visualize PCA Results - Variables

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Displays the correlation circle for descriptors and the supplementary 'hedonic' variable on the first two principal components.

```R
plot.PCA(res_pcamoy, axes=c(1, 2), choix="var", col.var="black", col.quanti.sup="blue", label=c("var", "quanti.sup"), 
  lim.cos2.var=0, title="")
```

--------------------------------

### ARDI: Automatic Research of DIvergences

Source: http://sensominer.free.fr/ardi.htm

This endpoint computes the highest differences between all categories of the variables 'product' and 'panelist' and their combinations, with respect to a set of quantitative variables (sensory descriptors). It involves mean centering and scaling of data, followed by calculating and sorting divergences.

```APIDOC
## POST /ardi/divergences

### Description
Computes the highest differences between all categories of the variables 'product' and 'panelist' and their combinations, with respect to a set of quantitative variables (sensory descriptors).

### Method
POST

### Endpoint
/ardi/divergences

### Parameters
#### Request Body
- **quantitative_variables** (array) - Required - A list of quantitative sensory descriptors.
- **qualitative_variables** (object) - Required - An object containing 'product' and 'panelist' arrays.
  - **product** (array) - Required - List of product categories.
  - **panelist** (array) - Required - List of panelist categories.

### Request Example
```json
{
  "quantitative_variables": ["descriptor1", "descriptor2"],
  "qualitative_variables": {
    "product": ["productA", "productB"],
    "panelist": ["panelist1", "panelist2"]
  }
}
```

### Response
#### Success Response (200)
- **tab** (dataframe) - Descriptors are mean centered per panelist and scaled to unit variance.
- **panelist** (dataframe) - The 10 highest divergences between panelists according to the sensory descriptors.
- **product** (dataframe) - The 10 highest divergences between products according to the sensory descriptors.
- **combination** (dataframe) - The 10 highest divergences between panelists and products according to the sensory descriptors.

#### Response Example
```json
{
  "tab": "[dataframe content]",
  "panelist": "[dataframe content]",
  "product": "[dataframe content]",
  "combination": "[dataframe content]"
}
```
```

--------------------------------

### Perform PMFA in R

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/napping.txt

Executes Partial Multiple Factor Analysis on the provided dataset.

```R
#PMFA
cock_nappe.pmfa = pmfa(
cock_nappe[,c("X1", "Y1", "X2", "Y2", "X3", "Y3", "X4", "Y4", "X5", "Y5", "X6", "Y6", "X7", "Y7", "X8", "Y8", "X9", "Y9", "X11", "Y11", "X12", "Y12")],
matrice.illu = NULL, coord=c(1,2), lim = c(60,40), graph.mfa=1,graph.ind=1,dilat=TRUE)
```

--------------------------------

### COCKTAIL Data

Source: http://sensominer.free.fr/cocktail.htm

Details about the COCKTAIL dataset, which includes three parts: compo.cocktail, senso.cocktail, and hedo.cocktail.

```APIDOC
## COCKTAIL Data

### Description
The data used here refer to 16 cocktails. They can be divided into three data sets: **compo.cocktail** (the cocktails composition), **senso.cocktail** (the cocktails sensory description) and **hedo.cocktail**(the hedonic scores).
1) **compo.cocktail** : the mango, banana, orange and lemon concentration are known 
2) **senso.cocktail** : each cocktail was evaluated by 12 panelists according to 13 sensory descriptors
3) **hedo.cocktail** : each cocktail was evaluated on a structured scale from 0 to 10, by 100 consumers, according to their liking (0) or disliking (10)

### Format
1) **compo.cocktail** : a data frame with 16 rows and 4 columns (one for each ingredient)
2) **senso.cocktail** : a data frame with 16 rows and 13 columns, each cocktail was evaluated according to 13 sensory descriptors
3) **hedo.cocktail** : a data frame with 16 rows (16 cocktails) and 100 columns: one column per consumer

### Usage
```R
data(cocktail)
```
```

--------------------------------

### Calculate and visualize panelist performance

Source: http://sensominer.free.fr/paneliperfresr2.htm

Uses the paneliperf function to assess panelist consistency and visualizes the results with coltable.

```R
data(chocolates)  
res<-paneliperf(chocolates, formul = "~ Product + Panelist + Session + Product:Panelist + Product:Session + Panelist:Session", formul.j = "~Product", col.j = 1, firstvar = 5, synthesis = TRUE)  
resr2<-magicsort(res$r2.ind, method = "median", ascending = FALSE)  
coltable(resr2, level.lower = 0.00, level.upper = 0.85, main.title = "Adjusted R-square (by panelist)")
```

--------------------------------

### Perform Decategorization Analysis

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Conducts a decategorization analysis to identify significant relationships between descriptors and categorical variables (Juice, Panelist) in the dataset.

```R
resdecat=decat(classique[,c("Session", "Panelist", "Rank", "Juice", "color.intensity", "odor.intensity", "strongness", 
  "sweet", "acidity", "bitterness", "odor.orange", "odor.banana", "odor.mango", "odor.lemon", "persistence", "pulp", 
  "thickness","hedonic")],firstvar=5,formul=~Juice+Panelist+Juice:Panelist,proba=0.05,graph=TRUE,col.lower="mistyrose",
  col.upper="lightblue")
resdecat
```

--------------------------------

### INDSCAL Model for Napping Data

Source: http://sensominer.free.fr/indscal.htm

Constructs the Indscal model specifically adapted for Napping data type, where product coordinates from panelists are used as input.

```APIDOC
## INDSCAL: Construct the Indscal model for Napping data type

### Description
This version of the Indscal model is specially adapted to Napping data type, i.e. products (stimuli) are positioned on a tableclothe by panelists, then their coordinates are used as input for the Indscal model.

### Method
`indscal`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```R
indscal(napping.don, napping.words)
```

### Response
#### Success Response (200)
Returns a list including the following elements:
- `W` (matrix): a matrix with the subject coordinates
- `points` (matrix): a matrix with the stimuli (individuals) coordinates
- `subvar` (vector): the strain between each configuration and the stimuli configuration
- `r2` (numeric): the strain criterion

#### Response Example
None provided
```

--------------------------------

### AVERAGETABLE: Computes a (products,descriptors) matrix

Source: http://sensominer.free.fr/averagetable.htm

Computes the (products,descriptors) matrix with entries representing the means over panelists and sessions. It can also compute analyses of variance for a given model and a set of quantitative variables. The function returns a data matrix where rows correspond to categories of a specified categorical variable (typically the product variable) and columns correspond to quantitative variables, with cells containing adjusted means or means.

```APIDOC
## POST /websites/sensominer_free_fr/averagetable

### Description
Computes the (products,descriptors) matrix with entries the means over panelists and sessions. Computes analyses of variance automatically for a given model and a set of quantitative variables. Returns a data matrix where each row is associated with each category of a given categorical variable (in most cases, the categorical variable is the _product_ variable), each column is associated with a quantitative variable, and each cell is the corresponding adjusted mean or mean.

### Method
POST

### Endpoint
/websites/sensominer_free_fr/averagetable

### Parameters
#### Query Parameters
- **method** (string) - Optional - Specifies the calculation method. Can be "coeff" for adjusted means or "mean" for means per category.
- **formul** (string) - Optional - Defines the analysis of variance model. Must begin with the categorical variable of interest (e.g., "~Product+Panelist+Product:Panelist"). If method is "mean", this can be restricted to the sole variable of interest (e.g., "~Product").
- **firstvar** (integer) - Optional - Specifies the starting column for quantitative variables.

### Request Body
- **data** (object) - Required - The input data, typically a data frame or matrix.

### Request Example
```json
{
  "data": "chocolates",
  "method": "coeff",
  "formul": "~Product+Panelist",
  "firstvar": 5
}
```

### Response
#### Success Response (200)
- **matrix** (object) - A matrix of dimension (p,q), where p is the number of categories of the qualitative variable of interest and q is the number of (sensory) descriptors. If method is "coeff", returns adjusted means; if method is "mean", returns means per category.

#### Response Example
```json
{
  "matrix": {
    "ProductA": {"Descriptor1": 4.5, "Descriptor2": 3.2},
    "ProductB": {"Descriptor1": 4.1, "Descriptor2": 3.5}
  }
}
```
```

--------------------------------

### MFA with Supplementary Word Frequencies

Source: http://sensominer.free.fr/desc-napping.html

This code performs MFA using word frequencies as supplementary variables. The 'f' type is used for frequencies, and group configurations should match the data structure.

```R
res.mfa<-MFA (BiscMots[,c(2:91)], group=c(rep(2,18),54), type = c(rep("c",18),"f"),  
            ncp = 36, name.group = c(c(1:18),"mots"), num.group.sup = (19),axes = c(1,2))  
            #type : "f" for frequencies 
```

--------------------------------

### Perform PCA on Averaged Data

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Conducts Principal Component Analysis on averaged product data, designating 'hedonic' as a supplementary quantitative variable.

```R
res_pcamoy=PCA(average[1:16,-(1:2)],quanti.sup=14:14)
```

--------------------------------

### PANELPERF: Panel's performance discrimination

Source: http://sensominer.free.fr/panelperf.htm

Computes P-values for the F-test and residual term in an analysis of variance model to discriminate between products based on panel performance.

```APIDOC
## PANELPERF

### Description
Computes automatically P-values associated with the F-test as well as the residual term for a given analysis of variance model.

### Method
Not specified (likely a function call within a statistical software)

### Endpoint
Not applicable (function within a package)

### Parameters
This function does not appear to have explicit parameters in the provided text. It likely operates on data structures defined within the statistical environment.

### Request Example
Not applicable

### Response
Returns a list including the following elements:

#### Success Response
- **p.value** (matrix) - A matrix of dimension (k,m) of P-values associated with the F-test for the k descriptors and the m factors and their combinations considered in the analysis of variance model of interest.
- **variability** (matrix) - A matrix of dimension (k,m) where the entries correspond to the percentages of variability due to the effects introduced in the analysis of variance model of interest.
- **res** (vector) - A vector of dimension k of residual terms for the analysis of variance model of interest.
- **r2** (vector) - A vector of dimension k of r-squared for the analysis of variance model of interest.

#### Response Example
```json
{
  "p.value": [[...]],
  "variability": [[...]],
  "res": [...],
  "r2": [...]
}
```
```

--------------------------------

### Compute Adjusted Means with AVERAGETABLE

Source: http://sensominer.free.fr/averagetable.htm

Use this when you need to compute the adjusted means for a given analysis of variance model, typically with products and panelists. Ensure the 'chocolates' dataset and 'firstvar' parameter are correctly specified.

```R
averagetable(chocolates, formul="~Product+Panelist", firstvar=5)
```

--------------------------------

### Perform HCPC in R

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/napping.txt

Performs Hierarchical Clustering on Principal Components based on an existing MFA result.

```R
#HCPC
res.HCPC<-HCPC(res,nb.clust=-1)
res.HCPC
```

--------------------------------

### Identify Representative Variables

Source: http://sensominer.free.fr/studies/GLORION_LEREST_MANDON/Rcode/classic.txt

Extracts and saves the most representative variables from the PCA results for the first two dimensions.

```R
var_mieux<-dimdesc(res_pcamoy, axes=c(1, 2))
write.infile(var_representative, file ="var_mieux_acp_moy.csv",append=FALSE)
```

--------------------------------

### Generate Barrow Plot

Source: http://sensominer.free.fr/barrowgraphe.htm

Creates a barrow plot from the coefficients of a decat analysis result. The color of the plot can be specified.

```R
barrow(resdecat$coeff, color = "orange")
```

--------------------------------

### Perform MFA on Napping Data

Source: http://sensominer.free.fr/desc-napping.html

Use the MFA function to analyze napping coordinates. Ensure data is correctly formatted and groups are defined. The 'type' argument should be 'c' for centered, unscaled continuous variables.

```r
res.mfa<-MFA (data[,c(2:37)], group=c(rep(2,18)), type = c(rep("c",18)),  
            ind.sup = NULL, ncp = 36, axes = c(1,2))  
            #data : the napping coordinates   
            #group : the different groups of the MFA   
            #type : the type of the variables in each group, "c" for centered (unscaled) continuous variables 
            
```

--------------------------------

### Analyze panelist agreement with paneliperf

Source: http://sensominer.free.fr/paneliperfagree.htm

Calculates panelist agreement using the chocolates dataset and visualizes the results with coltable.

```R
data(chocolates)  
res<-paneliperf(chocolates, formul = "~ Product + Panelist + Session + Product:Panelist + Product:Session + Panelist:Session", formul.j = "~Product", col.j = 1, firstvar = 5, synthesis = TRUE)  
resagree<-magicsort(res$agree, sort.mat = res$r2.ind, method = "median")  
coltable(resagree, level.lower = 0.00, level.upper = 0.85, main.title = "Agreement between panelists")
```
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